Marco Gori is professor of computer science at the University of Siena. His research interests are in the field of artificial intelligence, with emphasis on machine learning and game playing. In the last few years, he has been mainly involved in learning in relational domains in the continuum setting. He is interested in applying the conceived models to problems from pattern recognition and mining the web. He also very much likes discussions on novel models of computation and their relationships with human brain.
Deep learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns. Probabilistic logic reasoning, on the other hand, is capable to take consistent and robust decisions in complex environments. The integration of deep learning and logic reasoning is still an open-research problem and it is considered to be the key for the development of real intelligent agents. This paper presents Deep Logic Models, which are deep graphical models integrating deep learning and logic reasoning both for learning and inference. Deep Logic Models create an end-to-end differentiable architecture, where deep learners are embedded into a network implementing a continuous relaxation of the logic knowledge. The learning process allows to jointly learn the weights of the deep learners and the meta-parameters controlling the high-level reasoning. The experimental results show that the proposed methodology overtakes the limitations of the other approaches that have been proposed to bridge deep learning and reasoning.